> ## Documentation Index
> Fetch the complete documentation index at: https://docs.praison.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Embeddings

> Generate text embeddings using PraisonAI capabilities

## Overview

Generate vector embeddings for text using various embedding models through LiteLLM. Supports 100+ embedding models including OpenAI, Cohere, HuggingFace, Azure, and more.

## Quick Start

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import embedding

result = embedding("Hello, world!")
print(f"Dimensions: {len(result.embeddings[0])}")  # 1536
```

## Agent-Centric Usage

Embeddings are used internally by PraisonAI Agents for:

* **Knowledge retrieval** (RAG) - semantic search over documents
* **Memory storage** - storing and retrieving conversation context
* **Semantic similarity** - finding related content

### Agent with Knowledge (uses embeddings internally)

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent

agent = Agent(
    instructions="You are a helpful assistant.",
    knowledge=["AI agents can use tools to accomplish tasks."],
    knowledge_config={
        "embedder": {
            "provider": "openai",
            "config": {"model": "text-embedding-3-small"}
        }
    }
)

response = agent.chat("What can AI agents do?")
```

### Agent with Memory (uses embeddings internally)

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent

agent = Agent(
    instructions="You remember conversations.",
    memory=True,
    memory_config={"embedding_model": "text-embedding-3-small"}
)

agent.chat("My name is Alice.")
response = agent.chat("What is my name?")  # Uses embedding for retrieval
```

## Direct Embedding API

### Single Text Embedding

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import embedding

result = embedding("Hello, world!", model="text-embedding-3-small")
print(f"Dimensions: {len(result.embeddings[0])}")  # 1536
```

### Batch Embeddings

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import embedding

result = embedding(
    ["Hello", "World", "AI agents"],
    model="text-embedding-3-small"
)

print(f"Generated {len(result.embeddings)} embeddings")
```

### Get Model Dimensions

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import get_dimensions

dims = get_dimensions("text-embedding-3-small")  # 1536
dims = get_dimensions("text-embedding-3-large")  # 3072
```

### With Custom Dimensions

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import embedding

result = embedding(
    "Hello world",
    model="text-embedding-3-large",
    dimensions=256  # Reduce dimensions
)

print(f"Dimensions: {len(result.embeddings[0])}")  # 256
```

### Async Usage

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
import asyncio
from praisonaiagents import aembedding

async def main():
    result = await aembedding(
        "Hello world",
        model="text-embedding-3-small"
    )
    print(f"Dimensions: {len(result.embeddings[0])}")

asyncio.run(main())
```

## Import Options

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# Recommended: Direct import from praisonaiagents
from praisonaiagents import embedding, EmbeddingResult, get_dimensions
from praisonaiagents import aembedding  # async version

# Plural aliases (OpenAI style)
from praisonaiagents import embeddings, aembeddings

# Short aliases
from praisonaiagents import embed, aembed

# Alternative: Import from embedding submodule
from praisonaiagents.embedding import embedding, aembedding
```

## Parameters

| Parameter         | Type              | Default                  | Description         |
| ----------------- | ----------------- | ------------------------ | ------------------- |
| `input`           | str or List\[str] | Required                 | Text(s) to embed    |
| `model`           | str               | "text-embedding-3-small" | Embedding model     |
| `dimensions`      | int               | None                     | Output dimensions   |
| `encoding_format` | str               | "float"                  | "float" or "base64" |
| `timeout`         | float             | 600.0                    | Request timeout     |
| `api_key`         | str               | None                     | API key override    |

## Result Object

The `EmbeddingResult` object contains:

* `embeddings`: List of embedding vectors
* `model`: Model used
* `usage`: Token usage statistics

## Supported Models

Since PraisonAI wraps LiteLLM, all LiteLLM-supported embedding models work:

* **OpenAI**: `text-embedding-3-small`, `text-embedding-3-large`, `text-embedding-ada-002`
* **Cohere**: `cohere/embed-english-v3.0`, `cohere/embed-multilingual-v3.0`
* **Azure**: `azure/text-embedding-ada-002`
* **HuggingFace**: `huggingface/sentence-transformers/all-MiniLM-L6-v2`
* **Voyage**: `voyage/voyage-01`, `voyage/voyage-lite-01`
* And many more via LiteLLM